Evolving Hybrid Generalized Space-Time Autoregressive Forecasting with Cascade Neural Network Particle Swarm Optimization
نویسندگان
چکیده
Background: The generalized space-time autoregressive (GSTAR) model is one of the most widely used models for modeling and forecasting time series location data. Methods: In GSTAR model, there an assumption that research locations are heterogeneous. addition, differences between these shown in form a weighting matrix. novelty this paper we propose hybrid time-series uses cascade neural network obtains best parameters from particle swarm optimization. Results conclusion: This provides high accuracy value PM2.5, PM10, NOx, SO2 with forecasting, which justified by mean absolute percentage error (MAPE) around 0.01%.
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ژورنال
عنوان ژورنال: Atmosphere
سال: 2022
ISSN: ['2073-4433']
DOI: https://doi.org/10.3390/atmos13060875